Human performance oxygen sensor

ABSTRACT

A system for detecting unsafe equipment operation conditions using physiological sensors includes a plurality of wearable physiological sensors, each physiological sensor of the plurality of wearable physiological sensors configured to detect at least a physiological parameter of an operator of an item of equipment, and a processor in communication with the at least a physiological sensor and designed and configured to determine an equipment operation parametric model, wherein the equipment operation parametric rule relates physiological parameter sets to equipment operation requirements, detect using the equipment operation parametric model and the plurality of physiological parameters, a violation of an equipment operation requirement, and generate a violation response action in response to detecting the violation.

RELATED APPLICATION DATA

This application is a continuation in part of U.S. Nonprovisional patentapplication Ser. No. 15/492,612, filed on Apr. 20, 2017, and titled“HUMAN PERFORMANCE OXYGEN SENSOR,” the entirety of which is incorporatedherein by reference.

FIELD OF THE INVENTION

This invention relates to physiological sensing devices, and inparticular to human oxygen sensors and related systems and methods formeasuring physiological parameters.

BACKGROUND

Blood oxygen saturation can determine a plurality of physicalcharacteristics and ailments, including determining whether anindividual is on the verge of losing consciousness. Typically, sensorsmeasuring oxygenation are placed on the fingers or foreheads of patientsand do not include a means of analyzing the data and alerting the useror a third party of whether an issue has been determined.

SUMMARY OF THE DISCLOSURE

In one aspect a system for detecting unsafe equipment operationconditions using physiological sensors includes a plurality of wearablephysiological sensors, each physiological sensor of the plurality ofwearable physiological sensors configured to detect at least aphysiological parameter of an operator of an item of equipment, and aprocessor in communication with the at least a physiological sensor anddesigned and configured to determine an equipment operation parametricmodel, wherein the equipment operation parametric rule relatesphysiological parameter sets to equipment operation requirements, detectusing the equipment operation parametric model and the plurality ofphysiological parameters, a violation of an equipment operationrequirement, and generate a violation response action in response todetecting the violation.

In another aspect, a method of detecting unsafe equipment operationconditions using physiological sensors includes detecting, by processorin communication with a plurality of wearable physiological sensors, atleast a physiological parameter of an operator of an item of equipment,determining an equipment operation parametric model, wherein theequipment operation parametric rule relates physiological parameter setsto equipment operation requirements, detecting using the equipmentoperation parametric model and the plurality of physiologicalparameters, a violation of an equipment operation requirement, andgenerating a violation response action in response to detecting theviolation.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 shows aperspective view of adevice according to an embodimentdisclosed herein;

FIG. 2 shows a front view of adevice according to an embodimentdisclosed herein;

FIG. 3 shows a side view of a device according to an embodimentdisclosed herein;

FIG. 4 shows a perspective view of a device according to an embodimentdisclosed herein;

FIG. 5 shows a front sectional view of a device according to anembodiment disclosed herein;

FIG. 6 is a schematic illustration of an exemplary embodiment of anear-infrared spectroscopy sensor;

FIG. 7 is a schematic diagram of some aspects of user cranial anatomy inan embodiment;

FIG. 8 illustrates a block diagram of an embodiment of a systemincorporating a device according to an embodiment;

FIG. 9 illustrates a flow diagram of an embodiment of a method ofdetecting unsafe equipment operation conditions using physiologicalsensors; and

FIG. 10 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

DETAILED DESCRIPTION

In an embodiment, systems, devices and methods disclosed herein detectphysiological parameters such as blood oxygen level, blood pressure,neurological oscillations, and heart rate of a user who is operating anitem of equipment such as an aircraft through nonintrusive means.Sensors mounted in optimal locations on the head or neck of the user maydetect physiological parameters accurately, minimizing interference inactivities the user engages in while obtaining a clearer signal thanotherwise would be possible. Embodiments of the disclosed device mayprovide users such as pilots, firemen, and divers who are operatingunder extreme circumstances with an early warning regarding potentialcrises such as loss of consciousness, affording the user a few preciousextra seconds to avert disaster. Alarms may be provided to the user viabone-conducting transducers or by integration into displays the user isoperating, increasing the likelihood that the user will notice thewarning in time. Embodiments of devices, systems, and methods herein mayenable training for pilots or other persons to function withinphysiological limitations imposed by their environment, such ashypoxemia imposed by altitude, high G forces and the like; training mayfurther enable users to learn how to avoid total impairment, and tofunction under partial impairment.

Referring now to FIGS. 1-5, an exemplary embodiment of a perspectiveview (FIG. 1), a side view (FIG. 2), a front view (FIG. 3), aperspective view (FIG. 4), and a front sectional view (FIG. 5) of adevice for measuring physiological parameters 100 is illustrated.Referring now to FIG. 1, device for measuring physiological parameters100 includes a housing 104. Housing 104 may be mounted to an exteriorbody surface of a user; exterior body surface may include, withoutlimitation, skin, nails such as fingernails or toenails, hair, aninterior surface of an orifice such as the mouth, nose, or ears, or thelike. A locus on exterior body surface for mounting of housing 104and/or other components of device may be selected for particularpurposes as described in further detail below. Exterior body surfaceand/or locus may include an exterior body surface of user's head, face,or neck. Housing 104 may be constructed of any material or combinationof materials, including without limitation metals, polymer materialssuch as plastics, wood, fiberglass, carbon fiber, or the like. Housing104 may include an outer shell 108. Outer shell 108 may, for instance,protect elements of device 100 from damage, and maintain them in acorrect position on a user's body as described in further detail below.Housing 104 and/or outer shell 108 may be shaped, formed, or configuredto be inserted between a helmet worn on a head of the user and theexterior body surface; housing 104 and/or outer shell 108 may be shapedto fit between the helmet and the exterior body surface. As anon-limiting example, exterior body surface may be a surface, such as asurface of the head, face, or neck of user, which is wholly or partiallycovered by helmet, as described for example in further detail below. Asa further non-limiting example, housing 104 may be formed to have asimilar or identical shape to a standard-issue “ear cup” incorporated inan aviation helmet, so that housing 104 can replace ear cup after earcup has been removed; in an embodiment, device 100 may incorporate oneor more elements of ear-cup, including sound-dampening properties, oneor more speakers or other elements typically used to emit audio signalsin headsets or headphones, or the like. As a non-limiting example,device 100, housing 104, and/or shell may form a form-fit replacementfor standard earcups found in military flight helmets. Shell may berigid, where “rigid” is understood as having properties of an exteriorcasing typically used in an earcup, over-ear headphone, hearingprotection ear covering, or the like; materials used for such a shellmay include, without limitation, rigid plastics such as polycarbonateshell plastics typically used in helmets and hardhats, metals such assteel, and the like. Persons skilled in the art, upon reading theentirety of this disclosure, will understand “rigid” in this context assignifying sufficient resistance to shear forces, deformations, andimpacts to protect electronic components as generally required fordevices of this nature.

Still viewing FIGS. 1-5, housing 104 may include a seal 112 that restsagainst exterior body surface when housing 104 is mounted thereon. Seal112 may be pliable; seal 112 may be constructed of elastomeric, elastic,or flexible materials including without limitation flexible,elastomeric, or elastic rubber, plastic, silicone including medicalgrade silicone, gel, and the like. Pliable seal 112 may include anycombination of materials demonstrating flexible, elastomeric, or elasticproperties, including without limitation foams covered with flexiblemembranes or sheets of polymer, leather, or textile material. As anon-limiting example, pliable seal 112 may include any suitable pliablematerial for a skin-contacting seal portion of an earcup or other deviceconfigured for placement over a user's ear, including without limitationany pliable material or combination of materials suitable for use onheadphones, headsets, earbuds, or the like. In an embodiment, pliableseal 112 advantageously aids in maintaining housing 104 and/or othercomponents of device 100 against exterior body surface; for instance,where exterior body surface has elastomeric properties and may beexpected to flex, stretch, or otherwise alter its shape or position toduring operation, pliable seal 112 may also stretch, flex, or otherwisealter its shape similarly under similar conditions, which may have theeffect of maintaining seal 112 and/or one or more components of device100 as described in greater detail below, in consistent contact with theexterior body surface. Seal 112 may be attached to housing 104 by anysuitable means, including without limitation adhesion, fastening bystitching, stapling, or other penetrative means, snapping together orotherwise engaging interlocking parts, or the like. Seal 112 may beremovably attached to housing 104, where removable attachment signifiesattachment according to a process that permits repeated attachment anddetachment without noticeable damage to housing 104 and/or seal 112, andwithout noticeable impairment of an ability to reattach again by thesame process. As a non-limiting example, pliable seal 112 may be placedon an ear cup (for instance shown for exemplary purposes in FIG. 3) ofthe housing 104; pliable seal maybe formed of materials and/or in ashape suitable for use as an ear seal in an ear cup of a helmet, anover-ear headphone or hearing protection device, or the like. Personsskilled in the art, upon reviewing this disclosure in its entirety, willbe aware of forms and material properties suitable for use as seal 112,including without limitation a degree and/or standard of pliabilityrequired and/or useful to function as a seal 112 in this context.

With continued reference to FIGS. 1-5, housing 104 may include, beincorporated in, or be attached to an element containing additionalcomponents to device 100. For instance, in an embodiment, housing 104may include, be incorporated in, or be attached to a headset; headsetmay include, without limitation, an aviation headset, such as headsetsas manufactured by the David Clark company of Worcester Mass., orsimilar apparatuses. In some embodiments, housing 104 is headset; thatis, device 100 may be manufactured by incorporating one or morecomponents into the headset, using the headset as a housing 104. As afurther non-limiting example, housing 104 may include a mask; a mask asused herein may include any device or element of clothing that is wornon a face of user during operation, occluding at least a part of theface. Masks may include, without limitation, safety googles, gas masks,dust masks, self-contained breathing apparatuses (SCBA), self-containedunderwater breathing apparatuses (SCUBA), and/or other devices worn onand at least partially occluding the face for safety, functional, oraesthetic purposes. Housing 104 may be mask; that is, device 100 may bemanufactured by incorporating one or more elements or components ofdevice 100 in or on mask, using mask as housing 104. Housing 104 mayinclude, be incorporated in, or be attached to an element of headgear,defined as any element worn on and partially occluding a head or craniumof user. Headgear may wholly or partially occlude user's face and thusalso include a mask; headgear may include, for instance, a fullyenclosed diving helmet, space helmet or helmet incorporated in a spacesuit, or the like. Headgear may include a headband, such as withoutlimitation a headband of a headset, which may be an aviation headset.Headgear may include a hat. Headgear may include a helmet, including amotorcycle helmet, a helmet used in automobile racing, any helmet usedin any military process or operation, a construction “hardhat,” abicycle helmet, or the like. In an embodiment, housing 104 is shaped toconform to a particular portion of user anatomy when placed on exteriorbody surface; when placed to so conform, housing 104 may position atleast a sensor and/or user-signaling device 128 in a locus chosen asdescribed in further detail below. For instance, where housing 104 isincorporated in a helmet, mask, earcup or headset, housing 104 may bepositioned at a particular portion of user's head when helmet, mask,earcup or headset is worn, which may in turn position at least a sensorand/or user-signaling device 128 at a particular locus on user's head orneck.

Continuing to refer to FIGS. 1-5, device 100 includes at least aphysiological sensor 116. At least a physiological sensor 116 isconfigured to detect at least a physiological parameter and transmit anelectrical signal as a result of the detection; transmission of anelectrical signal, as used herein, includes any detectable alternationof an electrical parameter of an electrical circuit incorporating atleast a physiological sensor 116. For instance, at least a physiologicalsensor 116 may increase or reduce the impedance and/or resistance of acircuit to which at least a physiological sensor 116 is connected. Atleast a physiological sensor 116 may alter a voltage or current level,frequency, waveform, amplitude, or other characteristic at a locus incircuit. Transmission of an electrical signal may include modulation oralteration of power circulating in circuit; for instance transmissionmay include closing a circuit, transmitting a voltage pulse throughcircuit, or the like. Transmission may include driving a non-electricsignaling apparatus such as a device for transmitting a signal usingmagnetic or electric fields, electromagnetic radiation, optical orinfrared signals, or the like.

Still referring to FIGS. 1-5, at least a physiological parameter, asused herein, includes any datum that may be captured by a sensor, anddescribing a physiological state of user. At least a physiologicalparameter may include at least a circulatory and/or hematologicalparameter, which may include any detectable parameter describing thestate of blood vessels such as arteries, veins, or capillaries, anydatum describing the rate, volume, pressure, pulse rate, or other stateof flow of blood or other fluid through such blood vessels, chemicalstate of such blood or other fluid, or any other parameter relative tohealth or current physiological state of user as it pertains to thecardiovascular system. As a non-limiting example, at least a circulatoryparameter may include a blood oxygenation level of user's blood. Atleast a circulatory parameter may include a pulse rate. At least acirculatory parameter may include a blood pressure level. At least acirculatory parameter may include heart rate variability and rhythm. Atleast a circulatory parameter may include a plethysmograph describinguser blood-flow; in an embodiment, plethysmograph may describe areflectance of red or near-infrared light from blood. One circulatoryparameter may be used to determine, detect, or generate anothercirculatory parameter; for instance, a plethysmograph may be used todetermine pulse and/or blood oxygen level (for instance by detectingplethysmograph amplitude), pulse rate (for instance by detectingplethysmograph frequency), heart rate variability and rhythm (forinstance by tracking pulse rate and other factors over time), and bloodpressure, among other things. At least a physiological sensor may beconfigured to detect at least a hematological parameter of at least abranch of a carotid artery; at least a physiological parameter may bepositioned to capture the at least a hematological parameter byplacement on a location of housing that causes at least a physiologicalsensor to be placed in close proximity to the at least a branch; forinstance, where housing is configured to be mounted to a certainlocation on a user's cranium, and in a certain orientation, such as whenhousing forms all or part of a helmet, headset, mask, element ofheadgear, or the like, at least a physiological sensor may include asensor so positioned on the housing or an extension thereof that it willcontact or be proximate to a locus on the user's skin under which the atleast a branch runs. As a non-limiting example, where device 100 formsan earcup or earphone, at least a physiological sensor 116 may include asensor disposed on or embedded in a portion of the earcup and/orearphone contacting a user's skin over a major branch of the externalcarotid artery that runs near or past the user's ear.

In an embodiment, and still viewing FIGS. 1-5, detection ofhematological parameters of at least a branch of a carotid artery mayenable device 100 to determine hematological parameters of a user'scentral nervous system with greater accuracy than is typically found indevices configured to measure hematological parameters. For instance, ablood oxygen sensor placed on a finger or other extremity may detect lowblood oxygen levels in situations in which the central nervous system isstill receiving adequate oxygen, because a body's parasympatheticresponse to decreasing oxygen levels may include processes whereby bloodperfusion to the appendages is constricted in order to sustain higheroxygen levels to the brain; in contrast, by directly monitoring theoxygenation of a major branch of the external carotid artery, themeasurement of oxygenation to the central nervous system may be morelikely to achieve a more accurate indication of oxygen saturation than aperipheral monitor. Use of the carotid artery in this way may furtherresult in a more rapid detection of a genuine onset of hypoxemia; as aresult, a person such as a pilot that is using device 100 may be able tofunction longer under conditions tending to induce hypoxemia, knowingthat an accurate detection of symptoms may be performed rapidly andaccurately enough to warn the user. This advantage may both aid in andbe augmented by use with training processes as set forth in furtherdetail below.

With continued reference to FIGS. 1-5, at least a physiological sensor116 may include a hydration sensor; hydration sensor may determine adegree to which a user has an adequate amount of hydration, wherehydration is defined as the amount of water and/or concentration ofwater versus solutes such as electrolytes in water, in a person's body.Hydration sensor may use one or more elements of physiological data,such as sweat content and/or hematological parameters detected withoutlimitation using plethysmography, to determine a degree of hydration ofa user; degree of hydration may be associated with an ability to performunder various circumstances. For instance, a person with adequatehydration may be better able to resist the effects of hypoxemia inhigh-altitude and/or high-G for longer or under more severecircumstances, either because the person's body is better able torespond to causes of hypoxemia and delay onset, or because the person isbetter able to cope with diminished blood oxygen; this may be true ofother conditions and/or physiological states detected using at least aphysiological sensor 116, and may be detected using heuristics orrelationships derived, without limitation, using machine learning and/ordata analysis as set forth in further detail below.

Still referring to FIGS. 1-5, at least a physiological sensor 116 mayinclude a volatile organic compound (VOC) sensor. VOC sensor may senseVOCs, including ketones such as acetone; a user may emit ketones ingreater quantities when undergoing some forms of physiological stress,including without limitation hypoglycemia resulting from fasting oroverwork, which sometimes results in a metabolic condition known asketosis. As a result, detections of higher quantities of ketones mayindicate a high degree of exhaustion or low degree of available energy;this may be associated with a lessened ability to cope with otherphysiological conditions and/or parameters that may be detected by orusing at least a physiological sensor 116, such as hypoxemia, and/orenvironmental stressors such as high altitude or G-forces. Suchassociations may be detected or derived using data analysis and/ormachine learning as described in further detail below.

With continued reference to FIGS. 1-5, at least a physiologicalparameter may include neural oscillations generated by user neurons,including without limitation neural oscillations detected in the user'scranial region, sometimes referred to as “brainwaves.” Neuraloscillations include electrical or magnetic oscillations generated byneurological activity, generally of a plurality of neurons, includingsuperficial cranial neurons, thalamic pacemaker cells, or the like.Neural oscillations may include alpha waves or Berger's waves,characterized by frequencies on the order of 7.5-12.5 Hertz, beta waves,characterized by frequencies on the order of 13-30 Hertz, delta waves,having frequencies ranging from 1-4 Hertz, theta waves, havingfrequencies ranging from 4-8 Hertz, low gamma waves having frequenciesfrom 30-70 Hertz, and high gamma waves, which have frequencies from70-150 Hertz. Neurological oscillations may be associated with degreesof wakefulness, consciousness, or other neurological states of user, forinstance as described in further detail below. At least a sensor maydetect body temperature of at least a portion of user's body, using anysuitable method or component for temperature sensing.

Still referring to FIGS. 1-5, at least a physiological sensor 116 mayinclude an optical sensor, which detects light emitted, reflected, orpassing through human tissue. Optical sensor may include a near-infraredspectroscopy sensor (NIRS). A NIRS, as used herein, is a sensor thatdetects signals in the near-infrared electromagnetic spectrum region,having wavelengths between 780 nanometers and 2,500 nanometers. FIG. 6illustrates an exemplary embodiment of a NIRS 600 against an exteriorbody surface, which may include skin. NIRS 600 may include a lightsource 604, which may include one or more light-emitting diodes (LEDs)or similar element. Light source 604 may, as a non-limiting example,convert electric energy into near-infrared electromagnetic signals.Light source 604 may include one or more lasers. NIRS 600 may includeone or more detectors 608 configured to detect light in thenear-infrared spectrum. Although the wavelengths described herein areinfrared and near-infrared, light source 604 may alternatively oradditionally emit light in one or more other wavelengths, includingwithout limitation blue, green, ultraviolet, or other light, which maybe used to sense additional physiological parameters. In an embodiment,light source may include one or more multi-wavelength light emitters,such as one or more multi-wavelength LEDs, permitting detection ofblood-gas toxicology. Additional gases or other blood parameters sodetected may include, without limitation CO2 saturation levels, state ofhemoglobin as opposed to blood oxygen saturation generally. One or moredetectors 608 may include, without limitation, charge-coupled devices(CCDs) biased for photon detection, indium gallium arsenide (InGaAs)photodetectors, lead sulfide (PbS) photodetectors, or the like. NIRS 600may further include one or more intermediary optical elements (notshown), which may include dispersive elements such as prisms ordiffraction gratings, or the like. In an embodiment, NIRS 600 may beused to detect one or more circulatory parameters, which may include anydetectable parameter further comprises at least a circulatory parameter.At least a physiological sensor 116 may include at least two sensorsmounted on opposite sides of user's cranium.

Referring again to FIGS. 1-5, at least a physiological sensor 116 mayinclude a neural activity sensor. A neural activity sensor, as usedherein, includes any sensor disposed to detect electrical or magneticphenomena generated by neurons, including cranial neurons such as thoselocated in the brain or brainstem. Neural activity sensor may include anelectroencephalographic sensor. Neural activity sensor may include amagnetoencephalographic sensor. In an embodiment, neural activity sensormay be configured to detect neural oscillations. At least a sensor mayinclude an eye-tracking sensor, such as one or more cameras for trackingthe eyes of user. Eye-tracking sensor may include, as a non-limitingexample, one or more electromyographic (EMG) sensors, which may detectelectrical activity of eye muscles; electrical activity may indicateactivation of one or more eye muscles to move the eye and used by acircuit such as an alert circuit as described below to determine amovement of user's eyeball, and thus its current location of focus.

Continuing to refer to FIGS. 1-5, device 100 may communicate with one ormore physiological sensors that are not a part of device 100; one ormore physiological sensors may include any sensor suitable for use as atleast a physiological sensor 116 and/or any other physiological sensor.Communication with physiological sensors that are not part of device maybe accomplished by any means for wired or wireless communication betweendevices and/or components as described herein. Device may detect and/ormeasure at least a physiological parameter using any suitablecombination of at least a physiological sensor and/or physiologicalsensors that are not a part of device 100. Device 100 may combine two ormore physiological parameters to detect a physiological condition and/orphysiological alarm condition. For instance, and without limitation,where device 100 is configured to detect hypoxic incapacitation and/orone or more degrees of hypoxemia as described in further detail below,device 100 may perform such determination using a combination of heartrate and blood oxygen saturation, as detected by one or more sensor asdescribed above.

Still viewing FIGS. 1-5, at least a physiological sensor 116 may beattached to housing 104; attachment to housing 104 may include mountingon an exterior surface of housing 104, incorporation within housing 104,electrical connection to another element within housing 104, or thelike. Alternatively or additionally, at least a physiological sensor 116may include a sensor that is not attached to housing 104 or isindirectly attached via wiring, wireless connections, or the like. As anon-limiting example, at least a physiological sensor 116 and/or one ormore components thereof may be coupled to the pliable seal 112. In anembodiment, at least a physiological sensor 116 may be contactingexterior body surface; this may include direct contact with the exteriorbody surface, or indirect contact for instance through a portion of seal112 or other components of device 100. In an embodiment, at least aphysiological sensor 116 may contact a locus on the exterior bodysurface where substantially no muscle is located between the exteriorbody surface and an underlying bone structure, meaning muscle is notlocated between the exterior body surface and an underlying bonestructure and/or any muscle tissue located there is unnoticeable to auser as a muscle and/or incapable of appreciably flexing or changing itswidth in response to neural signals; such a locus may include, as anon-limiting example, locations on the upper cranium, forehead, nose,behind the ear, at the end of an elbow, on a kneecap, at the coccyx, orthe like. Location at a locus where muscle is not located betweenexterior body surface and underlying bone structure may decrease readinginterference and/or inaccuracies created by movement and flexing ofmuscular tissue. At least a physiological sensor 116 may contact a locushaving little or no hair on top of skin. At least a physiological sensor116 may contact a locus near to a blood vessel, such as a locus where alarge artery such as the carotid artery or a branch thereof, or a largevein such as the jugular vein, runs near to skin or bone at thelocation; in an embodiment, such a position may permit at least aphysiological sensor 116 to detect circulatory parameters as describedabove.

Referring now to FIG. 7, a schematic diagram of anatomy of a portion ofa user cranium 700 is illustrated for exemplary purposes. At least aphysiological sensor 116 may, for instance, be placed at or near to alocus adjacent to a branch 704 of a carotid artery, which may be abranch of an exterior carotid artery. At least a physiological sensor116 may be placed at a location 708 where substantially no muscle isfound between a user's skin and bone; such a location may be found, forinstance, near to the user's neck behind the ear. In an embodiment, atleast a physiological sensor may be placed in a locus that is bothadjacent to a branch 704 of a carotid artery and has substantially nomuscle between skin and bone. In an embodiment, measurement of at leasta physiological parameter, including without limitation pulseoxygenation and/or pulse rate as described in further detail below, on aparticular portion of the cranium may eliminate interfering factors suchas sweat and movement artifact; measurement above the neck may furthereliminate measurement issues experienced at the extremities (finger,wrist) due to temperature variation, movement and blood pooling under G.Where multiple physiological sensors of at least a physiological sensor116 are used, at least two sensors may be placed at two locations on auser's cranium; for instance, two sensors, one on each side of thecranium, may provide validation of consistent data, and assures a highcapture rate of data in flight. Two sensors may be so placed, as notedelsewhere in this disclosure, by form and/or configuration of housing104; for instance, housing 104 may include two earcups or other over-eardevices as described above.

As a non-limiting example of placement of at least a physiologicalsensor 116, and as illustrated for exemplary purposes in FIGS. 1-5, atleast a physiological sensor 116 may include a sensor mounted on an edgeof an earcup, and so positioned that placement of earcup over user's earplaces sensor in contact with user's head just behind the ear at a localskeletal eminence, with substantially no muscle tissue between skin andbone and a branch of the carotid artery nearby for detection ofcirculatory parameters. Similarly, where housing 104 includes a mask asdescribed above, a sensor of at least a physiological sensor 116 may bedisposed within mask at a location that, when mask is worn, placessensor against a forehead of user.

Still viewing FIGS. 1-5, where at least a physiological sensor 116includes a neural activity sensor, at least a physiological sensor 116may include one or more sensors placed in locations suitable fordetection of neural activity, such as on upper surfaces of a cranium ofuser, or similar locations as suitable for EEG or MEG detection andmeasurement.

With continued reference to FIGS. 1-5, device 100 may include aprocessor 120 in communication with the at least a physiological sensor.As used herein, a device, component, or circuit is “in communication”where the device, component, or circuit is able to receive data fromand/or transmit data to another device, component, or circuit. In anembodiment, devices are placed in communication by electrically couplingat least an output of one device, component, or circuit to at least aninput of another device, component, or circuit. Devices may further beplaced in communication by creating an optical, inductive, or othercoupling between two or more devices. Devices in communication may beplaced in near field communication with one another. Two or more devicesmay be in communication where the two or more devices are configured tosend and/or receive signals to or from each other. Placement of devicesin communication may include direct or indirect connection and/ortransmission of data; for instance, two or more devices may be connectedor otherwise in communication by way of an intermediate circuit.Placement of devices in communication with each other may be performedvia a bus or other facility for intercommunication between elements of acomputing device as described in further detail in this disclosure.Placement of devices in communication with each other may includefabrication together on a shared integrated circuit and/or wafer; forinstance, and without limitation, two or more communicatively coupleddevices may be combined in a single monolithic unit or module.

With continued reference to FIGS. 1-5, processor 120 may be constructedaccording to any suitable process or combination of processes forconstructing an electrical circuit; for instance, and withoutlimitation, processor 120 may include a printed circuit board. Processor120 may include a battery or other power supply; where processor 120 isintegrated in one or more other systems as described in further detailbelow, processor 120 may draw electrical power from one or more circuitelements and/or power supplies of such systems. Processor 120 mayinclude a memory; memory may include any memory as described below inreference to FIG. 11. Processor 120 may include one or more processorsas described in further detail below in reference to FIG. 11, includingwithout limitation a microcontroller or low-power microprocessor. In anembodiment, memory may be used to store one or more signals receivedfrom at least a physiological sensor 116.

Still referring to FIGS. 1-5, processor 120 may be in communication withat least an environmental sensor 124; at least an environmental sensor124 may be any sensor configured to detect at least an environmentalparameter, defined herein as a parameter describing non-physiologicaldata concerning user or surroundings of user, such as acceleration,carbon monoxide, or the like. At least an environmental sensor 124 mayinclude at least a motion sensor, including without limitation one ormore accelerometers, gyroscopes, magnetometers, or the like; at least amotion sensor may include an inertial measurement unit (IMU). At leastan environmental sensor 124 may include at least a temperature sensor.At least an environmental sensor 124 may include at least an air qualitysensor, such as without limitation a carbon monoxide sensor, or othersensor of any gas or particulate matter in air. At least anenvironmental sensor may include an atmospheric oxygen sensor, an oxygenflow meter, and/or a mask oxygen/CO2 sensor. At least an environmentalsensor 124 may include at least a barometric sensor. At least anenvironmental sensor 124 may include a pressure sensor, for instance todetect air or water pressure external to user. Processor 120 may beattached to housing 104, for instance by incorporation within housing104; as a non-limiting example and as shown in FIG. 5, the processor 120may be housed along an inner wall of the housing 104. Processor 120 maybe attached to an exterior of housing 104. According to an embodiment, acovering may be placed over housing 104, fully enclosing the processor120 within the housing 104; the enclosure may include a plastic, ametal, a mesh-type material, and/or any other suitable material.Processor 120 may be in another location not attached to or incorporatedin housing 104. Processor 120 may be incorporated into and/or connectedto one or more additional elements including any elements incorporatingor connected to user signaling devices as described in further detailbelow. As an alternative to storage of one or more parameter values suchas physiological parameters or environmental parameters in memory, alertcircuit may transmit the data to one or more remote storage mediumsthrough one or more wired and/or wireless means.

Still viewing FIGS. 1-5, processor 120 may be configured to receive atleast a signal from the at least a physiological sensor 116, generate analarm as a function of the at least a signal, and to transmit the alarmto a user-signaling device 128 in communication with the processor 120.Processor 120 may periodically sample data from at least a sensor; in anon-limiting example, data may be sampled 75 times per second;alternatively, or additionally, sampling of any sample and/or parametermay be event driven, such as a sensor that activates upon a threshold ofa sensed parameter being crossed, which may trigger an interrupt ofprocessor 120, or the like. In an embodiment, alarm is generated upondetection of any signal at all from at least a physiological sensor 116;for instance, at least a physiological sensor 116 may be configured onlyto signal processor 120 upon detection of a problematic or otherwisecrucial situation. Alternatively or additionally, processor 120 isfurther configured to detect a physiological alarm condition andgenerate the alarm as a function of the physiological alarm condition.In an embodiment, a physiological alarm condition includes anyphysiological condition of user that may endanger user or impair user'sability to perform an important task; as a non-limiting example, if useris flying an aircraft and user's physiological condition is such thatuser is unable to concentrate, respond rapidly to changing conditions,see or otherwise sense flight controls or conditions, or otherwisesuccessfully operate the aircraft within some desired tolerance of idealoperation, a physiological alarm condition may exist, owing to thepossibility of inefficient or dangerous flight that may result.Similarly, if user's physiological condition indicates user isexperiencing or about to experience physical harm, is losing or is aboutto lose consciousness, or the like, a physiological alarm condition mayexist.

Still referring to FIGS. 1-5, processor 120 may be configured to performany embodiment of any method and/or method step as described in thisdisclosure. For instance, and without limitation, processor 120 may bedesigned and configured to detect at least a flight condition having acausative association with hypoxemia, measure, using at least aphysiological sensor, at least a physiological parameter associated withhypoxemia, and determine, by the processor 120, and based on the atleast a physiological parameter, a degree of pilot hypoxemia.

In an embodiment, and still viewing FIGS. 1-5, detection of aphysiological alarm condition may include comparison of at least aphysiological parameter to a threshold level. For instance, and withoutlimitation, detection of the physiological alarm condition furthercomprises determination that the at least a physiological parameter isfalling below a threshold level; as an example, blood oxygen levelsbelow a certain cutoff indicate an imminent loss of consciousness, asmay blood pressure below a certain threshold. Similarly detection of aphysiological alarm condition may include detection of alpha waveactivity falling below a certain point, which may indicate entry intoearly stages of sleep or a hypnogogic state, and/or entry intounconsciousness. Comparison to threshold to detect physiological alarmcondition may include comparison of at least a physical parameter to avalue stored in memory, which may be a digitally stored value;alternatively or additionally comparison may be performed by analogcircuitry, for instance by comparing a voltage level representing atleast a physical parameter to a reference voltage representing thethreshold, by means of a comparator or the like. Threshold may representor be represented by a baseline value. Detection of a physiologicalalarm condition may include comparison to two thresholds; for instance,detection that incapacitation and/or loss of consciousness due tohypoxemia is imminent may include detection that a user's heart rate hasexceeded one threshold for heart rate and simultaneous or temporallyproximal detection that blood oxygen saturation has fallen below asecond threshold. Threshold or thresholds used for such comparison todetect a physiological alarm condition may include universal and/ordefault thresholds. For instance, device 100 may be set, prior to usewith a particular individual, with thresholds corresponding to a typicaluser's response to physiological conditions. For instance, device 100may initially store a threshold in memory of device 100 of 70% bloodoxygen saturation, as indicating that a typical user is likelyincapacitated by hypoxemia when blood oxygen saturation of that user,including blood oxygen saturation in a cranial vessel such as a branchof a carotid artery, has fallen below 70%; however, data gatheredregarding a particular user may indicate that the particular user isonly likely to be incapacitated at 65% blood oxygen saturation and/orthat the particular user is likely to be incapacitated at 75% bloodoxygen saturation, and threshold may be modified in memory accordingly.

Still referring to FIGS. 1-5, in an embodiment, a single physiologicalparameter and/or combination of physiological parameters may beassociated with a plurality of thresholds indicating a plurality ofdegrees of physiological conditions, such as degrees of incapacitation.As a non-limiting example, a plurality of thresholds may be storedregarding blood oxygen saturation, such as without limitation a firstthreshold indicating a possible saturation problem, a second indicatinga degree of blood oxygen saturation consistent with some degree ofperformance degradation on the part of the user, and a third thresholdindicating that incapacitation is likely. By way of illustration, andwithout limitation, default or factory-set thresholds may include afirst threshold triggered upon a user crossing into 80-90% blood oxygensaturation, indicating “saturation possible problem,” a second thresholdupon the user crossing into 70-80% saturation, indicating “Performancedegraded,” and a third threshold upon the user crossing into <70%saturation indicating “incapacitation likely,” while 90-100% saturationmay indicate a normal amount of blood oxygen saturation. Generally,multiple thresholds may be set just above physiologically-relevantlevels corresponding to onset of symptoms, cognitive impairment, andtotal incapacitation for a very-accurate, user-specific warning tone.User-specific thresholds at any tier or degree of incapacitation may beset and/or adjusted according to an iterative process, where usersdefine thresholds, and/or the system finds user thresholds based on, asa non-limiting example, user-specific training and/or sortie data.Determination that of an alarm state such as alarm states associatedwith one or more thresholds as described above may alternatively oradditionally be performed without a threshold comparison, for instanceby identifying a correlation of two or more sensor data determined, forinstance using machine learning as described below, to be associatedwith entry into such one or more alarm states; as a non-limitingexample, detection of imminent incapacitation and/or unconsciousness dueto hypoxemia may be accomplished by detecting a simultaneous ortemporally correlated increase in heart rate and decrease in bloodoxygen saturation. Combinations or associations of sensor data mayfurther involve measuring several human performance metrics includingSPO2, Pulse Rate, and full plethysmograph as well as environmentalsensor data such as flight conditions for full characterization andcorrelation of human performance in flight, for instance as described infurther detail below.

Still referring to FIGS. 1-5, detection of physiological alarm conditionmay include comparing at least a physiological parameter to at least abaseline value and detecting the physiological alarm condition as afunction of the comparison. At least a baseline value may include anumber or set of numbers representing normal or optimal function ofuser, a number or set of numbers representing abnormal or suboptimalfunction of user, and/or a number or set of numbers indicating one ormore physiological parameters demonstrating a physiological alarmcondition. At least a baseline value may include at least a threshold asdescribed above. In an embodiment, at least a baseline value may includea typical user value for one or more physiological parameters. Forexample, and without limitation, at least a baseline value may include ablood oxygen level, blood pressure level, pulse rate, or othercirculatory parameter, or range thereof, consistent with normal or alertfunction in a typical user; at least a baseline value may alternativelyor additionally include one or more such values or ranges consistentwith loss of consciousness or impending loss of consciousness in atypical user. Similarly, at least a baseline value may include a rangeof neural oscillations typically associated in users with wakeful oralert states of consciousness, and/or a range of neural oscillationstypically associated with sleeping or near-sleeping states, loss ofconsciousness or the like. Processor 120 may receive a typical uservalue and using the typical user value as the baseline value; forinstance, processor 120 may have typical user value entered into memoryof processor 120 by a user or may receive typical user value over anetwork or from another device. At least a baseline value may bemaintained in any suitable data structure, including a table, database,linked list, hash table, or the like.

Continuing to refer to FIGS. 1-5, typical user value may include a uservalue matched to one or more demographic facts about user. For instance,a pulse rate associated with loss of consciousness in women may not beassociated with loss of consciousness in men, or vice-versa; where useris a woman, the former pulse rate may be used as a baseline value forpulse rate. Baseline value may similarly be selected using a typicalvalue for persons matching user's age, sex, height, weight, degree ofphysical fitness, physical test scores, ethnicity, diet, or any othersuitable parameter. Typical user baseline value may be generated byaveraging or otherwise aggregating baseline values calculated per useras described below; for instance, where each user has baseline valuesestablished by collection of physiological parameters using devices suchas device 100, such values may be collected, sorted according to one ormore demographic facts, and aggregated to produce a typical userbaseline value to apply to user. Still referring to FIGS. 1-5, baselinevalue may be created by collection and analysis of at least aphysiological parameter; collection and/or analysis may be performed byprocessor 120 and/or another device in communication with processor 120.For instance, receiving a baseline value may include collecting aplurality of samples of the at least a physiological parameter andcalculating the baseline value as a function of the plurality ofsamples. Device 100 may continuously or periodically read or samplesignals from at least a physiological sensor 116, recording the results;such results may be timestamped or otherwise co-associated, such thatpatterns concerning physiological parameters may be preserved, detected,and/or analyzed. For example and without limitation, user pulse rateand/or blood pressure may vary in a consistent manner with blood oxygenlevel; user blood pressure and/or pulse rate may further vary in aconsistent manner with brain wave activity. Additional information fromother sensors may similarly collected to form baseline value; forinstance, where user is operating a machine, such as an aircraft, dataconcerning operation, such as flight control data, may be collected andassociated with at least a physiological parameter. As a non-limitingexample, user's reaction time when operating an aircraft may bemeasurably slower when user's blood pressure is below a certain amount,while showing no particular change for variations in blood pressureabove that amount. Additional information may further be provided byuser and/or another person evaluation user behavior and/or performance.For example, during test flights or other operation of an aircraft whereuser and/or aircraft may be observed, user, a supervisor, or anotherobserver may record information such as the user's performance, theuser's feelings or apparent state of health, the performance of theaircraft, and the like. Some factors that may be relatively objectivelymonitored regarding the overall state of health experience by the usermay include how many times the user has to use “anti-G” breathingexercises, or similar activities. In an embodiment, data is receivedfrom user and/or observers via numerical ratings, or selections ofbuttons or other entry devices that map to numerical ratings.Alternatively or additionally, entries may be formed using one or moretext entries; text entries may be mapped to numerical ratings or thelike using, as a non-limiting example, natural language analysis,textual vector analysis, or the like. Plurality of physiologicalparameters and/or user entries and other entries may be collected overtime, during, for instance a series of routine activities by user.

Continuing to refer to FIGS. 1-5, baseline value may be generated bycollection of data from at least an environmental sensor 124. Forinstance, each set of one or more physiological parameters taken at aparticular moment, or over a particular period of time, may be linked inmemory to one or more environmental parameters, including withoutlimitation motion-sensor data, air quality data, and the like. This maybe used by device 100, as a non-limiting example, to collectrelationships between environmental parameters and physiologicalparameters, such as a relationship between localized or systemic bloodpressure, G-forces, and state of consciousness of a user in an aircraft,or a relationship between quality of neural oscillations and externalwater pressure in a diver. This in turn may be used to produceadditional baseline information as described in further detail below. Asfurther examples, relationships determined to achieve baseline valuesmay include comparisons of heart rate, heart rate increase and heartrate recovery are easily compared to scientifically derived normsestablished in academia and professional athletics. Relationships mayinclude correlation of blood oxygen saturation, heart rate and heartrate variability. These metrics may be useful for objectivelydetermining deliberate risk levels associated with human performance,for instance using population data and/or machine learning as describedin further detail below. In an embodiment, a baseline study of eachindividual performance against known conditions, such as in theRestricted Oxygen Breathing Device, may be performed prior to use ofdevice 10; a purpose of the baseline evaluation may be to assess howeach individual responds to specific conditions. Such a response may beused to both validate the data to draw usable conclusions, as well as tocalibrate the alarm system to provide meaningful data while reducing theincidence of false alarms, for instance by setting and/or adjustingdefault threshold levels as described above.

With continued reference to FIGS. 1-5, plurality of physiologicalparameters, plurality of environmental parameters, and/or user-entereddata may be aggregated, either independently or jointly. For instance,device 100 may calculate an average level, for one or more parameters ofat least a physiological parameter, associated with normal or optimalfunction, health, or performance of user; a standard deviation from theaverage may also be calculated. This may be used, e.g., to generate analarm indicating that, for instance, a given physiological parameter hasrecently shifted more than a threshold amount from its average value.Threshold amount may be determined based on amounts by which a typicaluser may deviate from average amount before experiencing discomfort,loss of function, or loss of consciousness. Threshold amount may be setas some multiple of standard deviations, as calculated from sensedphysiological parameters; for instance, two or more standard deviationsfrom an average value for a given detected physiological parameter maytrigger an alarm.

Alternatively or additionally, and still referring to FIGS. 1-5,aggregation may include aggregation of relationships between two or moreparameters. For instance, and without limitation, aggregation maycalculate a relationship between a first physiological parameter of theat least a physiological parameter and a second physiological parameterof the at least a physiological parameter; this relationship may becalculated, as a non-limiting example, by selecting a first parameter asa parameter associated with a desired state for the user and a secondparameter known or suspected to have an effect on the first parameter.For example, first parameter may be blood oxygen level, and secondparameter may be blood pressure, such as localized blood pressure in acranial region; a reduction in cranial blood pressure may be determinedto be related to a reduction in cranial blood oxygen level, which inturn may be related to loss of consciousness or other loss of functionin user or in a typical user. As another example, aggregation maycalculate a relationship between a physiological parameter of the atleast a physiological parameter and an environmental parameter. Forexample, blood oxygen level may be inversely related to an amount ofacceleration or G force a user is experiencing in an aircraft; thisrelationship may be directly calculated from those two values, orindirectly calculated by associating the amount of acceleration or Gforce with a degree of decrease in cranial blood pressure, which maythen be related to blood oxygen levels. Aggregation may calculate arelationship between at least a physiological parameter and user-entereddata; for instance, people observing user may note losses of performanceor apparent function at times associated with a certain degree ofdecrease in blood oxygen level or some other physiological parameter.The relationships may be between combinations of parameters: forinstance, loss of function may be associated with an increase in Gforces coupled with a decrease in pulse rate, or a decrease in bloodoxygen coupled with a decrease in alpha waves, or the like.

Still referring to FIGS. 1-5, relationships between two or more of anyof physiological parameters, environmental parameters, and/oruser-entered parameters may be determined by one or moremachine-learning algorithms. A “machine learning process” or“machine-learning algorithm,” as used in this disclosure, is a processthat automatedly uses a body of data known as “training data” and/or a“training set” to generate an algorithm that will be performed by acomputing device/module to produce outputs given data provided asinputs; this is in contrast to a non-machine learning software programwhere the commands to be executed are determined in advance by a userand written in a programming language. Machine learning may function bymeasuring a difference between predicted answers or outputs and goalanswers or outputs representing ideal or “real-world” outcomes the otherprocesses are intended to approximate. Predicted answers or outputs maybe produced by an initial or intermediate version of the process to begenerated, which process may be modified as a result of the differencebetween predicted answers or outputs and goal answers or outputs.Initial processes to be improved may be created by a programmer or useror may be generated according to a given machine-learning algorithmusing data initially available. Inputs and goal outputs may be providedin two data sets from which the machine learning algorithm may derivethe above-described calculations; for instance a first set of inputs andcorresponding goal outputs may be provided and used to create amathematical relationship between inputs and outputs that forms a basisof an initial or intermediate process, and which may be tested againstfurther provided inputs and goal outputs. Data sets representing inputsand corresponding goal outputs may be continuously updated withadditional data; machine-learning process may continue to learn fromadditional data produced when machine learning process analyzes outputsof “live” processes produced by machine-learning processes. As anon-limiting example, an unsupervised machine-learning algorithm may beperformed on training sets describing co-occurrences of any or allparameters in time; unsupervised machine-learning algorithm maycalculate relationships between parameters and such co-occurrences. Thismay produce an ability to predict a likely change in a physiologicalparameter as a function of detected changes in one or more environmentalparameters; thus, a physiological alarm condition may be detected when aset of alarm parameters are trending in a way associated with decreasesin blood oxygen, causing a blood oxygen warning to be generated beforeany decrease in blood oxygen is detected. With continued reference toFIGS. 1-5, a supervised machine learning algorithm may be used todetermine an association between one or more detected parameters and oneor more physiological alarm conditions or other outcomes or situationsof interest or concern. For instance, a supervised machine-learningalgorithm may be used to determine a relationship between one or moresets of parameters, such as physiological parameters, environmentalparameters, and/or user-entered information, and one or morephysiological alarm conditions. To illustrate, a mathematicalrelationship between a set of physiological and environmental parametersas described above and a loss of consciousness, or near loss ofconsciousness, by user, may be detected by a supervised machine-learningprocess; such a process may include a linear regression process, forinstance, where a linear combination of parameters may is assumed to beassociated with a physiological alarm condition, and collected parameterdata and associated data describing the physiological alarm conditionare evaluated to determine the linear combination by minimizing an errorfunction relating outcomes of the linear combination and the real-worlddata. Polynomial regression may alternatively assume one or morepolynomial functions of parameters and perform a similar minimizationprocess. Alternatively or additionally neural net-based algorithms orthe like may be used to determine the relationship.

Still viewing FIGS. 1-5, each of the above processes for aggregationand/or machine learning may further be compared to test data, such asdata gathered concerning user physiological parameters, performance,and/or function, in one or more testing facilities or protocols; suchfacilities or protocols may include, for instance, centrifuge testing ofa user's response to acceleration and/or G forces, tests administered tomonitor one or more physiological parameters and/or user function orperformance under various adverse conditions such as sleep deprivation,boredom, and the like, or any other tests administered to determine theeffect of various conditions on user. Such test data may be collectedusing device 100, or alternatively may be collected using one or moreother devices, medical facilities, and the like. Any aggregation and/ormachine learning as described above may be applied to test data,independently or combined with other data gathered as described above;for instance, in an embodiment, test data may be combined with typicaluser data to achieve a first baseline, which may be compared to furtherdata gathered as described above to modify the baseline and generate asecond baseline using any suitable aggregation or machine-learningmethodology. Collected and/or aggregated data may be provided to users,such as supervisors or commanders, who may use collected and/oraggregated data to monitor state of health of individual users or groupsof users. In an embodiment, device 100 may store data collected during aperiod of activity, such as a flight where device 100 is used with apilot and may provide such data to another device upon completion of theperiod of activity. For instance, device 100 may download stored datainto a file for storage and tracking; data file may be analyzed using anindigenously designed application to determine areas of further study,allowing a detailed look at portions of ground operations or flight inwhich physio-logical responses can be compared to known conditions. Fileand/or collected data may be transferred to a remote computing devicevia network, wired, or wireless communication; for instance, and withoutlimitation, device 100 may be connected to or placed in communicationwith remote device after each flight or other period of activity. Wheredevice 100 is incorporated in an element of headgear such as a helmet,headset, and/or mask, such element of headgear may be connected viawired, wireless, and/or network connection to remote device.

With continued reference to FIGS. 1-5, in an illustrative example,detection of a physiological alarm condition may include determination,by the processor 120, that the user is losing consciousness.Alternatively or additionally, detection may include determination thatuser is about to lose consciousness. This may be achieved by comparingone or more physiological parameters and/or environmental parameters toa relationship, threshold, or baseline, which may be any relationship,threshold, or baseline as described above; for instance and withoutlimitation, where blood oxygen level drops below a threshold percentageof a baseline level, below an absolute threshold amount, below a certainnumber of standard deviations, or the like, processor 120 may determinethat user is about to lose consciousness or is losing consciousness, andissue an alarm. Alternatively or additionally, aggregation as describedabove may determine that imminent loss of consciousness is predicted bya particular set of values for one or more parameters as describedabove, processor 120 may detect a physiological alarm condition bydetecting the particular set of values, indicating that user is about tolose consciousness. In an embodiment, determination of user state and/orphysiological alarm condition may filter out anomalous or transientreadings, or readings altered by motion of one or more elements ofuser's body or environment; for instance, determination may includedetermination of a particular parameter value for longer than apredetermined amount of time.

As another example, and still viewing FIGS. 1-5, detection of thephysiological alarm condition further comprises determination that theuser is falling asleep; this may occur, for instance, where a neuralactivity sensor detects that a user is entering into an early stage ofsleep, or “dozing off,” for instance by detection of a change inbrainwaves. In an embodiment, processor 120 may generate an alarm wherealpha wave activity drops by a threshold percentage, by a thresholdamount, or the like; alternatively or additionally, one or more sets ofbrainwave patterns determined by processor 120 to be associated withuser falling asleep, for instance by aggregation or machine-learningmethods as described above, may be detected by processor 120 via atleast a neural activity sensor, triggering an alarm. This may, as anon-limiting example, aid in preventing a commercial pilot who is notactively operating flight controls from partially or wholly fallingasleep, which is a particular concern on long flights.

With continued reference to FIGS. 1-5, detection of a physiologicalalarm condition may further include detection of at least anenvironmental parameter, and detection of physiological alarm conditionas a function of the at least an environmental parameter. For instance,aggregation and/or machine learning processes as described above maydetermine that a reduction in cranial blood pressure coupled with anincrease in acceleration indicates a probable loss of consciousness in auser; an alarm may therefore be triggered by detection, by processor120, of that combination of decreased cranial blood pressure andincreased acceleration.

Still viewing FIGS. 1-5, processor 120 may incorporate or be incommunication with at least a user-signaling device 128. In anembodiment, at least a user-signaling device 128 may be incorporated indevice 100; for instance, at least a user-signaling device 128 may beattached to or incorporated in housing 104. Where at least auser-signaling device 128 contacts an exterior body surface of user,housing 104 may act to place at least a user-signaling device 128 incontact exterior body surface of user. Alternatively or additionally,device 100 may communicate with a user-signaling device 128 that is notincorporated in device 100, such as a display, headset, or other deviceprovided by a third party or the like, which may be in communicationwith processor 120. User-signaling device 128 may be or incorporate adevice for communication with an additional user-signaling device suchas a vehicle display and/or helmet avionics; for instance,user-signaling device 128 may include a wireless transmitter ortransponder in communication with such additional devices. In anembodiment, and without limitation, user-signaling device 128 may beconfigured to indicate the degree of pilot hypoxemia to at least a user,as described in further detail below.

Continuing to refer to FIGS. 1-5, at least a user-signaling device 128may include any device capable of transmitting an audible, tactile orvisual signal to a user when triggered to do so by processor 120. In anembodiment, and as a non-limiting example, at least a user-signalingdevice 128 may include a bone-conducting transducer in vibrationalcontact with a bone beneath the exterior body surface. A bone-conductingtransducer, as used herein, is a device or component that converts anelectric signal to a vibrational signal that travels through bone placedin contact with the device or component to an inner ear of user, whichinterprets the vibration as an audible signal. Bone-conductingtransducer may include, for instance, a piezoelectric element, which maybe similar to the piezoelectric element found in speakers or headphones,which converts an electric signal into vibrations. In an embodiment,bone-conducting transducer may be mounted to housing 104 in a positionplacing it in contact with a user's bone; for instance, where housing104 includes or is incorporated in an ear cup, housing 104 may placebone-conducting transducer in contact with user's skull just behind theear, over the sternocleidomastoid muscle. Likewise, where housing 104includes a headset, mask, or helmet, housing 104 may placebone-conducting transducer in contact with a portion of user's skullthat is adjacent to or covered by headset, mask, or helmet.

Still referring to FIGS. 1-5, at least a user-signaling device 128 mayfurther include an audio output device. Audio output device may includeany device that converts an electrical signal into an audible signal,including without limitation speakers, headsets, headphones, or thelike. As a non-limiting example, audio output device may include aheadset speaker of a headset incorporating or connected to device 100, aspeaker in a vehicle user is traveling in, or the like. At least auser-signaling device 128 may include a light output device, which maybe any device that converts an electrical signal into visible light;light output device may include one or more light source 604 s such asLEDs, as well as a display, which may be any display as described belowin reference to FIG. 11. At least a user-signaling device 128 mayinclude a vehicular display; at least a vehicular display may be anydisplay or combination of displays presenting information to a user of avehicle user is operating. For instance, at least a vehicular displaymay include any combination of audio output devices, light outputdevices, display screens, and the like in an aircraft flight console, acar dashboard, a boat dashboard or console, or the like; processor 120may be in communication with vehicular display using any form ofcommunicative coupling described above, including without limitationwired or wireless connection. At least a user-signaling device 128 mayinclude a helmet display; helmet display may include any visual, audio,or tactile display incorporated in any kind of helmet or headgear, whichmay be in communication with processor 120 according to any form ofcommunicative coupling as described above.

Still viewing FIGS. 1-5, user-signaling device 128 and/or processor 120may be programmed to produce a variety of indications, which maycorrespond to various physiological alarm conditions and/or contexts.Possible indications may be, but are not limited to: imminentunconsciousness, substandard oxygenation, erratic pulse, optimumoxygenation, and/or any other suitable indication, while maintaining thespirit of the present invention. Each such indication may have adistinct pattern of audible, visual, and/or textual indications; eachindication may include, for instance, an audible or textual warning ordescription of a physiological alarm condition. Any of the aboveuser-signaling devices 128 and/or signals may be used singly or incombination; for instance, a signal to user may include an audio signalproduced using a bone-conducting transducer, a verbal warning messageoutput by an audio output device, and a visual display of an image ortext indicating the physiological alarm condition. Persons skilled inthe art, upon reviewing the entirety of this disclosure, will be awareof various combinations of signaling means and/or processes that may beemployed to convey a signal to user. In an embodiment, in addition totransmitting an alarm to user-signaling device 128, alert circuit maytransmit a signal to one or more automated vehicular controls or othersystems to alleviate one or more environmental parameters contributingto physiological alarm condition. For instance, and without limitation,an automated aircraft control may receive an indication of hypoxia whilea motion sensor indicates high acceleration; aircraft control may reduceacceleration to alleviate the hypoxia. Persons skilled in the art, uponreviewing the entirety of this disclosure, may be aware of variousadditional ways in which automated systems may act to alleviate aphysiological alarm condition as described herein.

Referring now to FIG. 8, an embodiment of a system 800, which mayincorporate an embodiment of device 100, as illustrated. Device 100and/or any component thereof may be incorporated in system 800. Device100 may be any device as disclosed above; system includes a processor804, which may include any processor 120 as described above in referenceto FIGS. 1-7. Processor 804 is in communication with at least aphysiological sensor 116, which may include any physiological sensor asdescribed above in reference to FIGS. 1-7. System 800 may include and/orcommunicate with a user signaling device 128, which may include any usersignaling device 128 as described above in reference to FIGS. 1-8.System 800 may include a memory 808, which may be a solid-state memoryor the like; memory 808 may be used to record data during test periods,sorties, simulations, and the like, for instance as described above inreference to FIGS. 1-7. System 800 may include a power source 812, whichmay include without limitation a local power storage device such as abattery or fuel cell.

Still viewing FIG. 8, system 800 may communicate at times with anexternal device 816; communication may be continuous or episodic. Forinstance, and as described above in reference to FIGS. 1-8, device 100may communicate with external device 816 at the end of a session usingan item of equipment, such as a sortie in an aircraft, a work day and/orwork period, a dive, a shift, or the like; alternatively oradditionally, device 100 may communicate continuously with externaldevice 816 during at least a portion such as session, for instance toprovide information to a person coordinating activities, such as acommanding officer, supervisor, manager, foreman, or the like. Externaldevice 816 may alternatively or additionally include a deviceincorporated in a simulation environment, vehicle, aircraft, item ofequipment, or the like. System 800 may include or communicate with anexternal display 820. For instance, and without limitation externaldevice 816 may provide information to an external display 820 includinga monitor, audio communication device, or the like to a commandingofficer, person recording a simulation, or the like. External display820 may include a vehicular display as described above; vehiculardisplay may receive information from user signaling device 128 and/orother components of device 100 to provide information to user and/orpilot. Data may be relayed from external device 816 to further memorydevices and/or systems such as without limitation cloud storage 824;data may be analyzed in combination with additional data captured frompilot or other user, for instance during other sorties, simulations, ortest periods, from additional users, or the like, and may be analyzed asdescribed above to detect relationships between data detected byphysiological sensors and/or environmental sensors as described above.Any relationship between any element of data captured by one or morephysiological sensors, any element of data captured by one or moreenvironmental sensors, and/or any element of data concerning a flightcircumstance as described in further detail below, may be analyzed,calculated, and/or determined using machine learning and/or dataanalysis as described above.

Still referring to FIG. 8, system may be installed in, and/or system 800may include, an item of equipment 828. Item of equipment may include,without limitation, a vehicle, such as an aircraft, watercraft,submarine, terrestrial vehicle such as a truck, tank, armored vehicle,fire engine, a specialized machine such as a drill, a portable orstationary crane, an excavator, or the like, and/or a wearable and/oron-person life-support item such as an apparatus for SCBA, SCUBA, aReduced Oxygen Breathing Device (ROBD), a centrifuge or other devicethat varies G forces experienced by a user, a flight simulator, and/oran aircraft, including an aircraft the user and/or pilot is beingtrained to use.

Referring now to FIG. 9, an exemplary embodiment of a method 900 ofdetecting unsafe equipment operation conditions using physiologicalsensors is illustrated. At step 905, a processor in communication with aplurality of wearable physiological sensors detects at least aphysiological parameter of an operator of an item of equipment.Physiological sensors may include any physiological sensors as describedabove, where physiological sensors are configured to be worn upon theperson of a user, including without limitation mounting on or inclothing or gear worn for purposes of operating an item of equipment asset forth in further detail below. For instance, and without limitation,at least a physiological sensor may include a heart-rate sensor, a bloodoxygen sensor, a sensor configured to detect neural oscillations, and/ora VOC sensor.

At step 910, and still referring to FIG. 9, processor determines anequipment operation parametric model, wherein the equipment operationparametric rule relates physiological parameter sets to equipmentoperation requirements. Determining an equipment operation parametricmodel may include identifying, selecting, retrieving from program memorypiecemeal or in its entirety, any equipment operation parametric model;an “equipment operation parametric model,” as used herein, is any storedrelationship between combination of physiological and/or environmentalparameters, including without limitation physiological and/orenvironmental parameters as described above, and one or more violationsof an equipment operation requirements. An “equipment operationrequirement,” as used in this disclosure, is a requirement regarding thephysiological state of an operator that must be met for safe and/oreffective operation of item of equipment 828. Such a requirement may beselected based on a degree of likelihood that an operator will suffer aphysiological state violating the requirement. For instance, and withoutlimitation, a pilot of a military aircraft is likely to suffer fromoxygen deprivation and/or hypoxemia during a flight and/or sortie;oxygen deprivation and/or hypoxemia greater than a certain degree may beincompatible with safe operation of an aircraft, and may cause the pilotto crash the plane if left uncorrected. As a further example, anoperator of a piece of industrial machinery and/or equipment may be atsome risk, depending on conditions, to suffer hypothermia, heat stroke,sun stroke, dehydration, and/or stress-related effects, which may impairsuch an operator to the point where continued use of item of equipmentwould be unsafe for operator, equipment, or other persons. As anadditional example, a diver, such as a commercial diver, and/or a memberof a specialized construction profession such as a sand hog may be atrisk to suffer oxygen deprivation, oxygen toxicity, carbon dioxidetoxicity, nitrogen toxicity/intoxication, various neurological symptoms,panic, and/or decompression sickness, any of which in excess could leadto loss of life, failure to operate breathing apparatuses, lost ordamaged equipment, or the like. A violation of equipment operationrequirement may include any detected physiological state in which theuser is unable to meet a requirement and/or threshold consistent withminimally safe and/or effective equipment operation.

Still referring to FIG. 9, equipment operation parametric model mayinclude any threshold and/or set of thresholds for any physiologicalparameter, as described above; such thresholds and/or sets of thresholdsmay be selected, without limitation, as described above, including entryby experts and/or operators, as set according to statistical measures ofuser performance for all users, demographic groups of users, and/or fora current user from whom plurality of physiological parameters is beingmeasured in any embodiment of method 900. For instance, and withoutlimitation, system 800 and/or processor 804 may determines a degree ofpilot hypoxemia based on physiological parameter. For instance, wheremeasuring includes measuring at least a hematological parameterdetermining may include determining the at least a hematologicalparameter is associated with the level of hypoxemia; this may beaccomplished, without limitation, as described above. For instance,where measuring includes measuring a blood oxygen level, determining mayinclude determining that the detected blood oxygen level is associatedwith the level of hypoxemia. This may be performed according tothresholds indicating levels of probable degrees of impairmentassociated with various percentages of blood oxygen saturation asdescribed above; thresholds may include default thresholds set byfactory or according to typical users, and/or thresholds set accordingto user values, for instance using changes to default thresholds asdirected by data collected concerning user. As a further non-limitingexample, where the at least a hematological parameter includes a heartrate, determining may include determining that the detected heart rateis associated with a level of hypoxemia; for instance and withoutlimitation, an increase in heart rate, a change in blood pressure, orthe like may indicate a likely movement from one threshold to anotherregarding blood oxygen saturation levels. Where measuring includesmeasuring a heart rate and a blood oxygen level, which may be a bloodoxygen saturation level, determination may include determining that acombination of blood oxygen level and heart rate is associated with thelevel of hypoxemia; this may be performed as described above inreference to FIGS. 1-7.

Still referring to FIG. 9, a degree of hypoxemia may include anon-impairing degree of hypoxemia as described above; for instance,degree of hypoxemia may meet a threshold for a “possible problem,” whichmay also serve as an indication that blood oxygen condition of a pilotmay be likely to deteriorate further. Degree of hypoxemia may include animpairing degree of hypoxemia; an impairing degree of hypoxemia may, forinstance, relate to a second threshold as described above, for“performance degraded.” Degree of hypoxemia may include a degree ofhypoxemia associated with an imminent loss of consciousness. Degree ofhypoxemia may be determined by relationships between detected factorsand/or physiological parameters. For instance, and without limitation, adecrease in blood oxygen saturation of 5% by itself may not suffice totrip a threshold based on blood oxygen saturation alone, but aconcomitant increase in heart rate or decrease in blood pressure maycause processor 804 to determine that pilot has arrived at a higher ormore severe degree of hypoxemia. As a further non-limiting example, oneor more factors detected using at least a physiological sensor 116and/or at least an environmental sensor 124 may cause processor 804 totreat a given hematological or other parameter as indicating a more orless severe degree of hypoxemia; such factors may include, withoutlimitation, (1) detection of a degree of hydration of the pilot, where alower degree of hydration may be associated with more acute hypoxemiafor a given blood oxygen saturation level and/or heart rate; (2) adegree of pilot fatigue as determined, for instance, by brain waveactivity, history or length of recent activity, or the like, and where ahigher degree of fatigue or greater amount of recent flight activity maybe associated with a more severe level of hypoxemia for a given bloodoxygen saturation percentage and/or heart rate; (3) detected changes inneural oscillations, where, for instance, change indicating a tendencytoward drowsiness, and or indication of entry into early stages of sleepor the like, where greater drowsiness and/or incipient hypnagogic statesmay indicate a higher degree of hypoxemia for a given blood oxygensaturation level and/or heart rate; (4) detected changes in ketone orVOC emission by user, where greater ketone and/or VOC emission indicatesa higher degree of fatigue, which may be used as described above, and/ora more severe degree of hypoxemia for a given blood oxygen saturationlevel and/or heart rate; and/or (5) temperature, where a temperaturesignificantly higher or lower than room temperature may be associatedwith a more severe degree of hypoxemia for a given blood oxygensaturation percentage and/or heart rate. Each such factor, or anycombination thereof, may also be associated by processor 804 with agreater or lesser projected rate of degradation of pilot's degree ofhypoxemia; for instance, a more fatigued pilot, or less hydrated pilot,may be more likely to descend from a current level of hypoxemia to aworse level than a well-rested or adequately hydrated pilot. Acumulative fatigue model may be generated or applied to determine adegree to which pilot fatigue affects either a current level ofhypoxemia or a likely future rate of degradation. Degree of hypoxemiamay include a degree of generalized or systemic hypoxemia, and/or adegree of cerebral and/or cranial hypoxemia.

In an embodiment, and still referring to FIG. 9, operation parametricmodel may include a machine-learning model. Machine-learning model,and/or a machine-learning algorithm producing machine-learning model,may be trained and/or iteratively refined using training data. Trainingdata, as used herein, is data containing correlations that amachine-learning process may use to model relationships between two ormore categories of data elements. For instance, and without limitation,training data may include a plurality of data entries, each entryrepresenting a set of data elements that were recorded, received, and/orgenerated together; data elements may be correlated by shared existencein a given data entry, by proximity in a given data entry, or the like.Multiple data entries in training data may evince one or more trends incorrelations between categories of data elements; for instance, andwithout limitation, a higher value of a first data element belonging toa first category of data element may tend to correlate to a higher valueof a second data element belonging to a second category of data element,indicating a possible proportional or other mathematical relationshiplinking values belonging to the two categories. Multiple categories ofdata elements may be related in training data according to variouscorrelations; correlations may indicate causative and/or predictivelinks between categories of data elements, which may be modeled asrelationships such as mathematical relationships by machine-learningprocesses as described in further detail below. Training data may beformatted and/or organized by categories of data elements, for instanceby associating data elements with one or more descriptors correspondingto categories of data elements. As a non-limiting example, training datamay include data entered in standardized forms by persons or processes,such that entry of a given data element in a given field in a form maybe mapped to one or more descriptors of categories. Elements in trainingdata may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training datamay be provided in fixed-length formats, formats linking positions ofdata to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),enabling processes or devices to detect categories of data.

Alternatively or additionally, and still referring to FIG. 9. trainingdata may include one or more elements that are not categorized; that is,training data may not be formatted or contain descriptors for someelements of data. Machine-learning algorithms and/or other processes maysort training data according to one or more categorizations using, forinstance, natural language processing algorithms, tokenization,detection of correlated values in raw data and the like; categories maybe generated using correlation and/or other processing algorithms. As anon-limiting example, in a corpus of text, phrases making up a number“n” of compound words, such as nouns modified by other nouns, may beidentified according to a statistically significant prevalence ofn-grams containing such words in a particular order; such an n-gram maybe categorized as an element of language such as a “word” to be trackedsimilarly to single words, generating a new category as a result ofstatistical analysis. Similarly, in a data entry including some textualdata, a person's name may be identified by reference to a list,dictionary, or other compendium of terms, permitting ad-hoccategorization by machine-learning algorithms, and/or automatedassociation of data in the data entry with descriptors or into a givenformat. The ability to categorize data entries automatedly may enablethe same training data to be made applicable for two or more distinctmachine-learning algorithms as described in further detail below.Training data used by process or may correlate any input data asdescribed in this disclosure to any output data as described in thisdisclosure. As a non-limiting illustrative example training data mayinclude training data sets correlating sets of physiological and/orenvironmental parameters to violation of an equipment operationrequirement and/or data to be compared to thresholds to test forviolations of equipment operation requirements.

With continued reference to FIG. 9, processor, and/or a remote device incommunication with processor, may be designed and configured to create amachine-learning model using techniques for development of linearregression models. Linear regression models may include ordinary leastsquares regression, which aims to minimize the square of the differencebetween predicted outcomes and actual outcomes according to anappropriate norm for measuring such a difference (e.g. a vector-spacedistance norm); coefficients of the resulting linear equation may bemodified to improve minimization. Linear regression models may includeridge regression methods, where the function to be minimized includesthe least-squares function plus term multiplying the square of eachcoefficient by a scalar amount to penalize large coefficients. Linearregression models may include least absolute shrinkage and selectionoperator (LASSO) models, in which ridge regression is combined withmultiplying the least-squares term by a factor of 1 divided by doublethe number of samples. Linear regression models may include a multi-tasklasso model wherein the norm applied in the least-squares term of thelasso model is the Frobenius norm amounting to the square root of thesum of squares of all terms. Linear regression models may include theelastic net model, a multi-task elastic net model, a least angleregression model, a LARS lasso model, an orthogonal matching pursuitmodel, a Bayesian regression model, a logistic regression model, astochastic gradient descent model, a perceptron model, a passiveaggressive algorithm, a robustness regression model, a Huber regressionmodel, or any other suitable model that may occur to persons skilled inthe art upon reviewing the entirety of this disclosure. Linearregression models may be generalized in an embodiment to polynomialregression models, whereby a polynomial equation (e.g. a quadratic,cubic or higher-order equation) providing a best predicted output/actualoutput fit is sought; similar methods to those described above may beapplied to minimize error functions, as will be apparent to personsskilled in the art upon reviewing the entirety of this disclosure.

Continuing to refer to FIG. 9, machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naive Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Still referring to FIG. 9, models may be generated using alternative oradditional artificial intelligence methods, including without limitationby creating an artificial neural network, such as a convolutional neuralnetwork comprising an input layer of nodes, one or more intermediatelayers, and an output layer of nodes. Connections between nodes may becreated via the process of “training” the network, in which elementsfrom a training dataset are applied to the input nodes, a suitabletraining algorithm (such as Levenberg-Marquardt, conjugate gradient,simulated annealing, or other algorithms) is then used to adjust theconnections and weights between nodes in adjacent layers of the neuralnetwork to produce the desired values at the output nodes. This processis sometimes referred to as deep learning. This network may be trainedusing training data.

Further referring to FIG. 9, machine-learning algorithms may includesupervised machine-learning algorithms. Supervised machine learningalgorithms, as defined herein, include algorithms that receive atraining set relating a number of inputs to a number of outputs, andseek to find one or more mathematical relations relating inputs tooutputs, where each of the one or more mathematical relations is optimalaccording to some criterion specified to the algorithm using somescoring function. For instance, a supervised learning algorithm mayinclude combinations of physiological parameters and/or environmentalparameters as described above as inputs, violations of equipmentoperation requirements, and/or data comparable to thresholds fordetection thereof, as outputs, and a scoring function representing adesired form of relationship to be detected between inputs and outputs;scoring function may, for instance, seek to maximize the probabilitythat a given input and/or combination of elements inputs is associatedwith a given output to minimize the probability that a given input isnot associated with a given output. Scoring function may be expressed asa risk function representing an “expected loss” of an algorithm relatinginputs to outputs, where loss is computed as an error functionrepresenting a degree to which a prediction generated by the relation isincorrect when compared to a given input-output pair provided intraining data. Persons skilled in the art, upon reviewing the entiretyof this disclosure, will be aware of various possible variations ofsupervised machine learning algorithms that may be used to determinerelation between inputs and outputs.

Still referring to FIG. 9, supervised machine-learning processes mayinclude classification algorithms as described in further detail belowand defined as processes whereby a computing device derives, fromtraining data, a model for sorting inputs into categories or bins ofdata. Classification may be performed using, without limitation, linearclassifiers such as without limitation logistic regression and/or naiveBayes classifiers, nearest neighbor classifiers, support vectormachines, decision trees, boosted trees, random forest classifiers,and/or neural network-based classifiers.

With continued reference to FIG. 9, machine learning processes mayinclude unsupervised processes. An unsupervised machine-learningprocess, as used herein, is a process that derives inferences indatasets without regard to labels; as a result, an unsupervisedmachine-learning process may be free to discover any structure,relationship, and/or correlation provided in the data. Unsupervisedprocesses may not require a response variable; unsupervised processesmay be used to find interesting patterns and/or inferences betweenvariables, to determine a degree of correlation between two or morevariables, or the like.

Still referring to FIG. 9, machine-learning processes as described inthis disclosure may be used to generate machine-learning models. Amachine-learning model, as used herein, is a mathematical representationof a relationship between inputs and outputs, as generated using anymachine-learning process including without limitation any process asdescribed above, and stored in memory; an input is submitted to amachine-learning model once created, which generates an output based onthe relationship that was derived. For instance, and without limitation,a linear regression model, generated using a linear regressionalgorithm, may compute a linear combination of input data usingcoefficients derived during machine-learning processes to calculate anoutput datum. As a further non-limiting example, a machine-learningmodel may be generated by creating an artificial neural network, such asa convolutional neural network comprising an input layer of nodes, oneor more intermediate layers, and an output layer of nodes. Connectionsbetween nodes may be created via the process of “training” the network,in which elements from a training dataset are applied to the inputnodes, a suitable training algorithm (such as Levenberg-Marquardt,conjugate gradient, simulated annealing, or other algorithms) is thenused to adjust the connections and weights between nodes in adjacentlayers of the neural network to produce the desired values at the outputnodes. This process is sometimes referred to as deep learning.

With continued reference to FIG. 9, machine-learning model may furtherinclude a user-specific model; user specific model may include anymachine-learning model, as described in this disclosure, that isdeveloped using training data specific to a user, such as anyuser-specific data described above aggregated to develop user-specificbaselines or the like. A user-specific model may be trained solely usingsuch user-specific training data; alternatively or additionally,processor and/or a remote device may generate a machine-learning modelbased on data collected for a plurality of persons, such as personsmatching a one or more demographic category and/or grouping to whichuser belongs, and processor and/or a remote device may subsequentlytrain such a model with user-specific training data.

Still referring to FIG. 9, processor may determine equipment operationparametric model by retrieval from program memory and/or circuitry ofprocessor; there may be only one equipment operation parametric model inprogram memory, which may be loaded prior to and/or during operationfrom additional memory and/or a remote device according to any selectionand/or retrieval process described below. Alternatively or additionally,processor may select equipment operation parametric model from two ormore equipment operation parametric models stored in program and/orother memory. Selection may include receiving an identifier of item ofequipment 828, user, and/or one or more contextual data describingcircumstances of equipment operation.

As a non-limiting example, system 800 deployed in an aircraft may selectan equipment operation parametric model for an aircraft based on aflight condition. A flight condition, as used herein, is any set ofcircumstances in an aircraft, flight simulator, or other item ofequipment 828; at least a flight condition may include any state ofenvironment or modification to environment within a item of equipment828 that item of equipment 828 is commanded by a person or system toperform, including a change in air pressure, oxygen content,acceleration, direction, rotational or angular velocity, barometricpressure, variations in temperature, or the like. At least a flightcondition may include any condition detectable using at least anenvironmental sensor 124. At least a flight condition having a causativeassociation with hypoxemia may include any circumstance in a flightand/or flight simulation tending to cause either generalized orcranial/cerebral hypoxemia, including without limitation high G-forcesimposed by acceleration of an aircraft, motion of a centrifuge, or thelike, low atmospheric and/or respirator oxygen contents, low barometricpressure, and the like. Data analysis and/or machine learning asdescribed above may be used to detect relationships between flightconditions and hypoxemia.

Alternatively or additionally, and continuing to refer to FIG. 9,determining equipment operation parametric model may include performinga classification process to determine the equipment operation parametricmodel. A “classifier,” as used in this disclosure is a machine-learningmodel, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm”and/or “classification process” as described in further detail below,that sorts inputs into categories or bins of data, outputting thecategories or bins of data and/or labels associated therewith. Aclassifier may be configured to output at least a datum that labels orotherwise identifies a set of data that are clustered together, found tobe close under a distance metric as described below, or the like.Computing device 104 and/or another device may generate a classifierusing a classification algorithm, defined as a processes whereby acomputing device 104 derives a classifier from training data.Classification may be performed using, without limitation, linearclassifiers such as without limitation logistic regression and/or naiveBayes classifiers, nearest neighbor classifiers such as k-nearestneighbors classifiers, support vector machines, least squares supportvector machines, fisher's linear discriminant, quadratic classifiers,decision trees, boosted trees, random forest classifiers, learningvector quantization, and/or neural network-based classifiers.

Still referring to FIG. 9, processor and/or a remote device may beconfigured to generate a classifier using a Naive Bayes classificationalgorithm. Naive Bayes classification algorithm generates classifiers byassigning class labels to problem instances, represented as vectors ofelement values. Class labels are drawn from a finite set. Naive Bayesclassification algorithm may include generating a family of algorithmsthat assume that the value of a particular element is independent of thevalue of any other element, given a class variable. Naive Bayesclassification algorithm may be based on Bayes Theorem expressed asP(A/B)=P(B/A) P(A)+P(B), where P(AB) is the probability of hypothesis Agiven data B also known as posterior probability; P(B/A) is theprobability of data B given that the hypothesis A was true; P(A) is theprobability of hypothesis A being true regardless of data also known asprior probability of A; and P(B) is the probability of the dataregardless of the hypothesis. A naive Bayes algorithm may be generatedby first transforming training data into a frequency table. Computingdevice 104 may then calculate a likelihood table by calculatingprobabilities of different data entries and classification labels.Computing device 104 may utilize a naive Bayes equation to calculate aposterior probability for each class. A class containing the highestposterior probability is the outcome of prediction. Naive Bayesclassification algorithm may include a gaussian model that follows anormal distribution. Naive Bayes classification algorithm may include amultinomial model that is used for discrete counts. Naive Bayesclassification algorithm may include a Bernoulli model that may beutilized when vectors are binary.

With continued reference to FIG. 9, processor and/or a remote device maybe configured to generate a classifier using a K-nearest neighbors (KNN)algorithm. A “K-nearest neighbors algorithm” as used in this disclosure,includes a classification method that utilizes feature similarity toanalyze how closely out-of-sample-features resemble training data toclassify input data to one or more clusters and/or categories offeatures as represented in training data; this may be performed byrepresenting both training data and input data in vector forms, andusing one or more measures of vector similarity to identifyclassifications within training data, and to determine a classificationof input data. K-nearest neighbors algorithm may include specifying aK-value, or a number directing the classifier to select the k mostsimilar entries training data to a given sample, determining the mostcommon classifier of the entries in the database, and classifying theknown sample; this may be performed recursively and/or iteratively togenerate a classifier that may be used to classify input data as furthersamples. For instance, an initial set of samples may be performed tocover an initial heuristic and/or “first guess” at an output and/orrelationship, which may be seeded, without limitation, using expertinput received according to any process as described herein. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data. Heuristic mayinclude selecting some number of highest-ranking associations and/ortraining data elements.

Still referring to FIG. 9, generating k-nearest neighbors algorithm maygenerate a first vector output containing a data entry cluster,generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute l as derived using aPythagorean norm: l=√{square root over (Σ_(i=0) ^(n)a_(i) ²)}, wherea_(i) is attribute number i of the vector. Scaling and/or normalizationmay function to make vector comparison independent of absolutequantities of attributes, while preserving any dependency on similarityof attributes; this may, for instance, be advantageous where casesrepresented in training data are represented by different quantities ofsamples, which may result in proportionally equivalent vectors withdivergent values.

With continued reference to FIG. 9, inputs to classification process mayinclude an environmental parameter detected using an environmentalsensor; inputs may include a plurality of environmental parameters.Environmental sensor may include any environmental sensors and/orcombinations thereof as described above, including a motion sensor anatmospheric oxygen sensor, a pressure sensor, or the like.

Further referring to FIG. 9, and as a non-limiting example,machine-learning model may include a classifier, as defined above, thatinputs sets of physiological and/or environmental parameters and outputsviolations and/or data that may be compared to a threshold and/orrequirement. Alternatively or additionally, machine-learning algorithmmay include a regression model that inputs sets of physiological and/orenvironmental parameters and outputs violations and/or data that may becompared to a threshold and/or requirement; output may include, forinstance, a score calculated via an equation or other mathematicalfunction of parameters having coefficients determined via a regressionprocess, which may be compared to a threshold value. Any ofmachine-learning model, classifier, or the like may be generated byprocessor 804 and/or by a remote device 816; in the latter case,processor 804 may upload machine-learning model, classifier, or the likefrom remote device 816 in the form of software, memory files, firmware,or the like, either during or prior to use of item of equipment.

At step 915, and continuing to refer to FIG. 9, processor detects, usingthe equipment operation parametric model and the plurality ofphysiological parameters, a violation of an equipment operationrequirement. Detection may be performed using any form of thresholdcomparison and/or other comparison and/or detection method for any alertcondition as described above, for instance where the alert conditiondescribes and/or indicates a physiological condition and/or state thatviolates an equipment operation requirement. Detection may alternativelyor additionally include inputting one or more physiological parameters,one or more environmental parameters, and/or any combination thereof toa machine-learning model, receiving an output from the machine-learningmodel identifying a violation, and detecting the violation based on theoutput, including without limitation comparing a quantitative output toa threshold, for instance by comparing an output indicative of aprobability of suffering a degraded ability to an upper threshold, anoutput indicative of a probability of successful performance of animportant and/or critical function to a lower threshold indictive ofinability to perform adequately, or the like.

At step 920, and with continued reference to FIG. 9, processor generatesa violation response action in response to detecting the violation; thismay be performed according to any process and/or processes describedabove for generation and/or output of alerts. Violation response actionmay further include, without limitation stopping, pausing, and/orshutting off item of equipment 828 to prevent an accident, injury,damage to equipment, or other negative outcome. Violation responseaction may include transmitting a signal to another user and/orequipment item indicating that user and/or item of equipment 828 is inneed of assistance, unable to perform, in need of rescue, or otherwisein need of intervention

Still referring to FIG. 9, and as a non-limiting example, generation ofa violation response action may include generation of at least apilot-specific flight guideline, which may include determining anassociation between at least a physiological condition, at least aflight condition, and/or other information concerning pilot and/or otheruser. At least a pilot-specific flight guideline may be based onbaseline data regarding pilot, on one or more training or mission goals,or both. For instance, a goal of a training session may be for a pilotto operate under light (e.g., relating to a first threshold level asdescribed above) to moderate (e.g., relating to a second threshold levelas described above) hypoxemia, for a certain period of time intended,for instance, to indicate circumstances under which a mission-criticalor otherwise important maneuver or act must be performed at highaltitudes, high speeds, or other circumstances likely to induce at leasta degree of hypoxemia; length of period and/or degree of hypoxemiaexperienced may be set according to pilot's record of past performance,baselines recorded regarding that pilot's performance under light tomoderate hypoxemia and/or that pilot's tendency to degrade to higherdegrees of hypoxemia under some circumstances, or the like. Another goalmay, for instance be to have pilot undergo a particular environmentalcondition, such as atmospheric oxygen below a set level and/or a seriesof high-G maneuvers and/or periods, and to attempt and/or practicestrategies for avoiding incapacitation. A second instruction may issue,as well; for instance, if pilot is degrading more than expected, atraining session may be modified to be less severe or aborted. At leasta pilot-specific flight guideline may be provided to at least a userand/or a flight condition generation device 828 as an instruction, asdescribed above. As noted above, any data regarding past pilotperformance, baselines, and the like, together with correlatedphysiological parameters, environmental parameters, and/or flightconditions as described above may be analyzed using data analysis and/ormachine learning as described above, to derive mathematicalrelationships between various factors; such relationships can be used toset thresholds, for instance as described above, and to plan pilottraining and/or missions to remain within certain threshold ranges, toincrease pilot resistance to hypoxemia and extend such threshold ranges,or the like. In an embodiment, both device 100 and pilot may learn ineach mission and/or training session to work more effectively within thephysiological limits of the pilot, enabling a greater range of actionsto be performed to a higher degree of safety. Methods as describedherein and/or device 100 may enable training profiles to identifypotential shortfalls and/or difficulties, for instance by modifyingtraining profiles and/or plans to avoid detected episodes of hypoxemia,either for particular pilots 812, for particular cohorts or demographicsets of pilots, or for pilots in general.

As a further example, a violation response action may direct a device,such as an ROBD or the like, that adjusts physical conditions of a user,such as atmospheric oxygen levels and/or barometric pressure experiencedby a pilot, to adjust such a physical condition, such as withoutlimitation oxygen level and/or barometric pressure; such a command maybe transmitted and/or otherwise provided to training and processor 804as a flight condition. As a further example, where item of equipment 828is a device that varies G forces on a user, such as a centrifuge, itemof equipment 828 may receive or automatically generate a command toincrease and/or decrease G forces on user, such as a command to increaseand/or decrease angular velocity of a centrifuge; this command may betransmitted or otherwise provided to processor 804 as a flightcondition. Where item of equipment 828, one or more commands from user,system 800, autopilot guidance computer or instrumentation, or the likemay be provided to processor 804; for instance, pilot manual controls ina fly-by-wire or partial fly-by-wire aircraft may take the form ofelectronic signals, which may also be transmitted and/or provided toprocessor 804. Flight plan or precalculated trajectory data may beprovided to processor 804 as part of data describing at least a flightcondition; for instance, a fly-by-wire system may be programmed torespond to a particular pilot and/or autopilot command by causing anaircraft to traverse a certain trajectory at a certain velocity or witha certain acceleration.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 11 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 1100 withinwhich a set of instructions for causing a control system, such as thedevice 100 disclosed above, to perform any one or more of the aspectsand/or methodologies of the present disclosure may be executed. It isalso contemplated that multiple computing devices may be utilized toimplement a specially configured set of instructions for causing one ormore of the devices to perform any one or more of the aspects and/ormethodologies of the present disclosure. Computer system 1100 includes aprocessor 1104 and a memory 1108 that communicate with each other, andwith other components, via a bus 1112. Bus 1112 may include any ofseveral types of bus structures including, but not limited to, a memorybus, a memory controller, a peripheral bus, a local bus, and anycombinations thereof, using any of a variety of bus architectures.

Memory 1108 may include various components (e.g., machine-readablemedia) including, but not limited to, a random-access memory component,a read only component, and any combinations thereof. In one example, abasic input/output system 1116 (BIOS), including basic routines thathelp to transfer information between elements within computer system1100, such as during start-up, may be stored in memory 1108. Memory 1108may also include (e.g., stored on one or more machine-readable media)instructions (e.g., software) 1120 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 1108 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 1100 may also include a storage device 1124. Examples ofa storage device (e.g., storage device 1124) include, but are notlimited to, a hard disk drive, a magnetic disk drive, an optical discdrive in combination with an optical medium, a solid-state memorydevice, and any combinations thereof. Storage device 1124 may beconnected to bus 1112 by an appropriate interface (not shown). Exampleinterfaces include, but are not limited to, SCSI, advanced technologyattachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394(FIREWIRE), and any combinations thereof. In one example, storage device1124 (or one or more components thereof) may be removably interfacedwith computer system 1100 (e.g., via an external port connector (notshown)). Particularly, storage device 1124 and an associatedmachine-readable medium 1128 may provide nonvolatile and/or volatilestorage of machine-readable instructions, data structures, programmodules, and/or other data for computer system 1100. In one example,software 1120 may reside, completely or partially, withinmachine-readable medium 1128. In another example, software 1120 mayreside, completely or partially, within processor 1104.

Computer system 1100 may also include an input device 1132. In oneexample, a user of computer system 1100 may enter commands and/or otherinformation into computer system 1100 via input device 1132. Examples ofan input device 1132 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 1132may be interfaced to bus 1112 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 1112, and any combinations thereof. Input device 1132may include a touch screen interface that may be a part of or separatefrom display 1136, discussed further below. Input device 1132 may beutilized as a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 1100 via storage device 1124 (e.g., a removable disk drive, aflash drive, etc.) and/or network interface device 1140. A networkinterface device, such as network interface device 1140, may be utilizedfor connecting computer system 1100 to one or more of a variety ofnetworks, such as network 1144, and one or more remote devices 1148connected thereto. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A network,such as network 1144, may employ a wired and/or a wireless mode ofcommunication. In general, any network topology may be used. Information(e.g., data, software 1120, etc.) may be communicated to and/or fromcomputer system 1100 via network interface device 1140.

Computer system 1100 may further include a video display adapter 1152for communicating a displayable image to a display device, such asdisplay device 1136. Examples of a display device include, but are notlimited to, a liquid crystal display (LCD), a cathode ray tube (CRT), aplasma display, a light emitting diode (LED) display, and anycombinations thereof. Display adapter 1152 and display device 1136 maybe utilized in combination with processor 1104 to provide graphicalrepresentations of aspects of the present disclosure. In addition to adisplay device, computer system 1100 may include one or more otherperipheral output devices including, but not limited to, an audiospeaker, a printer, and any combinations thereof. Such peripheral outputdevices may be connected to bus 1112 via a peripheral interface 1156.Examples of a peripheral interface include, but are not limited to, aserial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, devices and/or software according to the present disclosure.Accordingly, this description is meant to be taken only by way ofexample, and not to otherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for detecting unsafe equipment operationconditions using physiological sensors, the system comprising: aplurality of wearable physiological sensors, each physiological sensorof the plurality of wearable physiological sensors configured to detectat least a physiological parameter of an operator of an item ofequipment; and a processor in communication with the at least aphysiological sensor, the processor designed and configured to:determine an equipment operation parametric model, wherein the equipmentoperation parametric rule relates physiological parameter sets toequipment operation requirements; detect using the equipment operationparametric model and the plurality of physiological parameters, aviolation of an equipment operation requirement; and generate aviolation response action in response to detecting the violation.
 2. Thesystem of claim 1, wherein the at least a physiological sensor furthercomprises a sensor configured to detect neural oscillations.
 3. Thesystem of claim 1, wherein the at least a physiological sensor furthercomprises a volatile organic compound sensor.
 4. The system of claim 1,wherein the equipment operation parametric model further comprises amachine-learning model.
 5. The system of claim 4, wherein themachine-learning model further comprises a user-specific model.
 6. Thesystem of claim 1, wherein determining the equipment operationparametric model further comprises performing a classification processto determine the equipment operation parametric model.
 7. The system ofclaim 6 further comprising an environmental sensor, wherein inputs tothe classification process include an environmental parameter detectedusing the environmental sensor
 8. The system of claim 7, wherein the atleast an environmental sensor includes at least a motion sensor.
 9. Thesystem of claim 7, wherein the at least an environmental sensor includesat least atmospheric oxygen sensor.
 10. The system of claim 7, whereinthe at least an environmental sensor includes at least a pressuresensor.
 11. A method of detecting unsafe equipment operation conditionsusing physiological sensors, the method comprising: detecting, byprocessor in communication with a plurality of wearable physiologicalsensors, at least a physiological parameter of an operator of an item ofequipment determining an equipment operation parametric model, whereinthe equipment operation parametric rule relates physiological parametersets to equipment operation requirements; detecting using the equipmentoperation parametric model and the plurality of physiologicalparameters, a violation of an equipment operation requirement; andgenerating a violation response action in response to detecting theviolation.
 12. The method of claim 11, wherein the at least aphysiological sensor further comprises a sensor configured to detectneural oscillations.
 13. The method of claim 11, wherein the at least aphysiological sensor further comprises a volatile organic compoundsensor.
 14. The method of claim 11, wherein the equipment operationparametric model further comprises a machine-learning model.
 15. Themethod of claim 14, wherein the machine-learning model further comprisesa user-specific model.
 16. The method of claim 11, wherein determiningthe equipment operation parametric model further comprises performing aclassification process to determine the equipment operation parametricmodel.
 17. The method of claim 16 further comprising an environmentalsensor, wherein inputs to the classification process include anenvironmental parameter detected using the environmental sensor
 18. Themethod of claim 17, wherein the at least an environmental sensorincludes at least a motion sensor.
 19. The method of claim 17, whereinthe at least an environmental sensor includes at least atmosphericoxygen sensor.
 20. The method of claim 17, wherein the at least anenvironmental sensor includes at least a pressure sensor.